Quantifying Teaching Behaviour in Robot Learning from Demonstration
This addresses the challenge of inefficient teaching for robot operators, though it is incremental as it builds on existing learning from demonstration methods.
The paper tackles the problem of measuring and improving human teaching effectiveness in robot learning from demonstration, showing that guided feedback can achieve 169-180% improvement in teaching efficiency compared to unguided teaching.
Learning from demonstration allows for rapid deployment of robot manipulators to a great many tasks, by relying on a person showing the robot what to do rather than programming it. While this approach provides many opportunities, measuring, evaluating and improving the person's teaching ability has remained largely unexplored in robot manipulation research. To this end, a model for learning from demonstration is presented here which incorporates the teacher's understanding of, and influence on, the learner. The proposed model is used to clarify the teacher's objectives during learning from demonstration, providing new views on how teaching failures and efficiency can be defined. The benefit of this approach is shown in two experiments (N=30 and N=36, respectively), which highlight the difficulty teachers have in providing effective demonstrations, and show how ~169-180% improvement in teaching efficiency can be achieved through evaluation and feedback shaped by the proposed framework, relative to unguided teaching.